Large-scale wireless biosensor networks for biomedical diagnostics

ABSTRACT

A method includes providing an ensemble of distributed sensors, delivering radio frequency (RF) power to each sensor by inductive near-field coupling by a magnetic field projected by an epidermal transmit (Tx) coil, in each individual sensor, detecting a sparse binary event in its immediate environment, reporting the detected sparse binary event to an external RF receiver hub asynchronously and with low latency, and minimizing error rates due to statistical data packet collisions in asynchronous telemetry by digitally encoding each sensor according to a particular address scheme where each address is one function from an infinite set of mathematically orthogonal functions, enabling a simultaneous detection from up to ten thousand points without interference at a common receiver.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit from U.S. Provisional Patent ApplicationSer. No. 63/257,829, filed Oct. 20, 2021, which is incorporated byreference in its entirety.

BACKGROUND OF THE INVENTION

The present invention relates generally to sensor networks, and moreparticularly to large-scale wireless biosensor networks for biomedicaldiagnostics.

In general, multipoint sensors are needed for many biomedical diagnosticapplications where each sensor records local activity from a complexphysiological circuit. In particular, wearable and implantable spatiallydistributed sensors can play a key role in collecting the informationneeded to reconstruct the physiological state dynamics of such circuitsas in the brain, in the heart, in muscles, and other internal organs.

Demonstrations of various sensors, aimed at either the central or theperipheral nervous system, have been limited to single or a smallhandful of devices as the technical challenge to build a large-scalemultipoint sensor require new innovations.

BRIEF SUMMARY OF THE INVENTION

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the invention. Thissummary is not an extensive overview of the invention. It is intended toneither identify key or critical elements of the invention nor delineatethe scope of the invention. Its sole purpose is to present some conceptsof the invention in a simplified form as a prelude to the more detaileddescription that is presented later.

In general, in one aspect, the invention features a method includingproviding an ensemble of distributed sensors, delivering radio frequency(RF) power to each sensor by inductive near-field coupling by a magneticfield projected by an epidermal transmit (Tx) coil, in each individualsensor, detecting a sparse binary event in its immediate environment,reporting the detected sparse binary event to an external RF receiverhub asynchronously and with low latency, and minimizing error rates dueto statistical data packet collisions in asynchronous telemetry bydigitally encoding each sensor according to a particular address schemewhere each address is one function from an infinite set ofmathematically orthogonal functions, enabling a simultaneous detectionfrom up to ten thousand points without interference at a commonreceiver.

In another aspect, the invention features a system including independentsensors, each of the independent sensors digitally encoded on-chipaccording to a particular address scheme, an epidermal transmit (Tx)coil, the Tx coil delivering radio frequency (RF) power to each of theplurality of independent sensors, the Tx coil capturing asynchronousdata emitted from each of the plurality of sensors by radio frequency(RF) backscattering, and an external radio frequency (RF) receiver hub.

In still another aspect, the invention features a method includingproviding a communication protocol between an external RF transceiverhub and an ensemble of distributed individual sensors, in eachindividual sensor, detecting a sparse binary event in its immediateenvironment, reporting the detected sparse binary event to an externalRF hub asynchronously and with low latency, and minimizing error ratesdue to statistical data packet collisions in asynchronous telemetry bydigitally encoding each sensor according to a particular address schemewhere each address is one function from an infinite set ofmathematically random or orthogonal functions, enabling the simultaneousdetection from up to ten thousand points without interference at thecommon receiver.

These and other features and advantages will be apparent from a readingof the following detailed description and a review of the associateddrawings. It is to be understood that both the foregoing generaldescription and the following detailed description are explanatory onlyand are not restrictive of aspects as claimed.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

These and other features, aspects, and advantages of the presentinvention will become better understood with reference to the followingdescription, appended claims, and accompanying drawings where:

FIG. 1 is a diagram.

FIG. 2 is a diagram.

FIG. 3 is a graph.

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FIG. 7 is a block diagram.

FIG. 8 illustrate block diagrams.

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FIG. 12 illustrate a block diagram and graphs.

FIG. 13 illustrate flow diagrams.

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FIG. 17 are diagrams.

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DETAILED DESCRIPTION OF THE INVENTION

The subject innovation is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present invention.

The present invention is a system and method to measure internal statesof the body by a large wireless network of spatially distributedunobtrusive sensors. The individual sensors are spatially distributedsilicon chiplets which are either implanted in the body or applied tothe surface of the skin. Physiological signals are measured from eachautonomous sensor locally and transmitted wirelessly to a commonradio-frequency (RF) antenna-receiver. To enable a large number ofsensors to stream their data in real time, the present inventionincludes specific telemetry protocols to achieve low error rates and lowlatency.

In FIG. 1 , an exemplary individual microprobe sensor is shown, herespecific to a brain implant. Each such device is an autonomous unit thateither records neural activity from nearby neurons or stimulates localcircuits by current injection. We have developed scalable and agilenetworking solutions for large ensembles of implantable neurograins. Anexternal ratio frequency (RF) hub coordinates bidirectional transmissionof digital data, further linking neurograin populations to downstreamcomputational platforms for decoding and encoding the data.

In the embodiment illustrated in FIG. 1 , each sensor in an ensemble ofthousands of such distributed devices, detects a biosignal of interestat one specific microscale location. Whenever any sensor detects asparse binary event in its immediate environment, such as, for example,a neural cell action potential, it reports the event to an external RFreceiver hub asynchronously and with low latency.

This “detect and immediate transmit” principle is shown schematically inFIG. 2 , enabling the entire network to achieve efficient multipointdigital transmission with minimal system simplicity and power. Errorrates due to statistical data packet collisions in the asynchronoustelemetry are minimized by digitally encoding each sensor according to aparticular address scheme. The telemetry subcircuits of the sensor ASICsare designed for binary phase shift key (BPSK) modulation. The approachbelow is termed an asynchronous sensor network, here designedspecifically for biomedical implants/wearables.

Suppose that we have an ensemble of autonomous microprobes with eachequipped with a unique electronic address. The analog circuit portion onthe chip is configured to accept neural signals from the pair ofmicroelectrodes (i.e., gradient in the neural potential near a neuron),filter lower frequency components and using a comparator subcircuit toset a threshold, record spikes only from the continuous background. Theon-board digital engine is designed to automatically transmit a spikingevent within a millisecond window as an “uplink” data packet to theexternal receiver. This scheme does not require any instructions fromthe external unit (i.e., no “downlink” commands), only RF powerdelivered to each neurograin by inductive near-field coupling via themagnetic field projected by the epidermal Tx coil (here near 1 GHz).

To illustrate the anticipated data streams arriving at the externalreceiver for an ensemble of imagined implanted neurograins, FIG. 3illustrates a superposition of raw spiking data from simultaneousrecording by 96 microelectrodes from a ‘Utah’ array, implanted into theauditory cortex of a macaque monkey. Using this type of data, we havesynthesized a hypothetical case of an aggregate of 960 simultaneouschannels (each neuron with a Poisson spike distribution and averagefiring rate of 20 spikes per second). This gives an estimate of roughlyfive spikes occurring every one millisecond, therefore framing therequirement for the data date in the telemetry for a network ofneurograins. Given the sparsity of spiking, even in a highly activecortical circuit, requires only an approximate bandwidth of 10 Mbps foran ensemble of many thousands of neurograins.

The challenge for a large ensemble of implanted microprobes, i.e.,reporting detected events (spikes) from up to a thousand distinctmicroscale locations through a single common transmission channel, is toidentify and separate the individual contributions in the received datafor subsequent analysis of the neural (or other phycological cellularlevel) population dynamics. On one hand, the (˜30 MHz) clocks on boardthe neurograins are independent and free running thereby lackingsynchrony even across an ensemble of nominally identical transmitters.On the other hand, the concept of an asynchronous detect and transmitparadigm for sparse events is attractive in its simplicity while themoderate bandwidth requirements offer the opportunity to operate alarge-scale network. Here we explicitly assume that each intracorticalmicroprobe will report detected spike events by transmitting a spreadbit sequence (bit ‘1’) and remains silent otherwise (bit ‘0’). Given theintrinsic sparsity of spike activity even in a highly active corticalcircuit (˜20 spikes/s per neuron on average; each chip operates at a lowduty cycle and switches on its uplink only when the detection circuitsrecognizes an above threshold event. We have explored various digitalmodulation schemes on test chips (e.g., ASK, BPSK, ASK-PWM) todemonstrate up to 10 Mbps speeds with various chip architectures, a datarate sufficient to accommodate to thousands neurograins.

FIG. 4 shows an example of a computer simulation where a BPSK modulatedRF backscattered sample neural data string is detected by IQdemodulation, and then reconstructed at high fidelity by the decodingalgorithm.

In an asynchronous digital transmission scheme, a main source of errorsoccurs due to data packet collisions. Statistically, any two neurograinscan detect and uplink their data nearly simultaneously, thus there isthe probability that data packets overlap, i.e., a partial packetcollision event occurs. This is a fundamental problem for a largeensemble of transmitters which use the same transmission channel.

We employ a particular packet encoding strategy to improve the bittransmission fidelity at the RF backend to minimize the error-rates dueto packet collisions on one hand while maximizing the number ofneurograins allowed in a network on the other hand. We have demonstratedthe utility of the on-chip PUF addressing method for both the recordingand stimulating versions of the epicortical (larger footprint) ASICs. Toenable scaling the multipoint system to large ensembles of microprobeswe add another layer in the digital identification. In particular we usean additional unique spreading code, embedded on each chip, namely theGold code in Code-division multiple access (CDMA)-type digitalcommunication. Mathematically, Gold codes have bounded smallcross-correlations within a finite set of codes such as used in mobilecommunications when multiple devices are broadcasting in the samefrequency range. In hardware on the chip scale, a Gold code can beimplemented in a linear shift registers (LSFR) architecture, in verysmall footprint circuits in the 65 nm RF CMOS process while requiringminimal digital processing on the chip itself.

Simulations have been carried out to test and compare the encoding ofPUF and Gold codes by different combinatorial modulation approaches inthe recovery of binary data such as spikes for large ensembles ofneurograins. FIG. 5 compares the recovery of RF backscattered BPSK, ASK,and ASK envelope data, respectively, and the associated auto—andcross-correlations for Gold code of order L=6. In this example, theautocorrelation for the BPSK IQ data appears to offer a better option.

We have compared the performance of PUF-codes and Gold-codes in terms ofstatistical transmission/demodulation bit error rates (BER). FIG. 6shows a sample test case assuming 1025 and 2049 neurograins in thenetwork, respectively. The simulation is shown for the BER as a functionof the code length of the PUF component, suggesting that a spreadingsequence of at least 1023 is needed (a relatively long code but stillwith acceptable system latency on a msec timescale. We note that the BER(fidelity of digital transmission) differs from the recently introduced‘spike error rate’ (SER), the latter describing statistical errors dueto sparse sampling by spike-detection circuit of neuronal firing.

For in-silico implementation of the theoretical network models, we showone exemplary chip design for the PUF-Gold spreading code, shown at ablock diagram level in FIG. 7 . In addition to a simplethreshold-detecting analog front end, the circuit embeds the codingscheme for a BPSK backscattering modulator, seeding a 1023-bit gold codewith a PUF seed.

In summary, the present invention is a RF-based communication approachfor a network of microchip sensors that is scalable to many thousands ofnodes. The method makes efficient use of the spectrum without the needfor global synchronization through a novel code modulation by aCDMA-type approach. The approach is inspired by principles ofinformation processing in the brain as understood today, where neuronalpackets of information are sparse, binary “spike firing” events. Hereeach sensor is a remotely powered, millimeter-scale, microwatt-powerintegrated circuit chip. One goal is to lay the engineering foundationfor an implantable sensor network to enable predictive modeling of statedynamics of a functional area of the brain cortex. More broadly, thetarget environment of interest may be another physiological circuit inthe human body, assets in a warehouse with a rapidly changing inventory,interaction-driven vehicular or human traffic pattern, and in general aheterogeneous interactive environment in forecasting future trajectoryis of importance. Our particular motivation is to develop wireless brainsensors for future application to brain-machine interfaces (BMI), anapplication where recording from a handful of implanted microelectrodeshas demonstrated the operation of external assistive devices by directcortical commands.

The ASBIT RF networking approach is shown in FIG. 8 for wirelesstransmission by ensembles of passive wireless sensors (Nodes X, Y, Z′ asshown in “a” of FIG. 8 ). Each sensor transmits data packaged with aunique on-chip encoded RF identifier and, crucially, backscatters onlywhen reporting an ‘action potential’ type event. The ASBIT idea isinspired by synaptic communication by ensembles of neurons firing actionpotentials in a brain network (“b” in FIG. 8 ) in being parallel,asynchronous, binary, yet sparse. Much unlike conventional code-divisionmultiple access (CDMA), however, the neuron-inspired ASBIT scheme doesnot transmit any non-events, i.e., “zeros”. Only meaningful information(“ones”) above an event threshold is transmitted. As a consequence, gooduse can be made of key network resources, whether in an RF, optical orother communication medium in terms of the spectrum, code, and timing(“c” in FIG. 8 ). We show below how a single ASBIT link is scalable upto tens of thousands of user nodes where the sparsity of the targetenvironment determines a fundamental limit for the system. A summary ofthe serial steps to unpack aggregate signals in the process of RFdemodulation is shown the example “d” of FIG. 8 , here for 1,000autonomous nodes. The raw quadrature modulated (I/Q) data at thereceiver (second panel from left) shows an aggregate simulatedsuperposed signal. Given that data is encoded at each sensor with aunique identifier, the binary events across the ensemble can unpacked bya demodulation technique, here using matched filters (MF).

The Gold Code is one of many choices was quasi-orthogonal cdes as thevehicle for the large-scale asynchronous sensor networks which can beemployed in this invention. As an introductory example specifically ofthe Gold code, FIG. 9 shows the simulated I/Q waveform of data receivedfrom one RF sensor (“a” in FIG. 9 ) and the computed auto-correlationtrace for its specific Gold code waveform while allowing for intrinsicresidual clock offset (“b” in FIG. 9 ). “c” in FIG. 8 showscross-correlation traces between this particular waveform and that ofanother Gold coded waveform, illustrating the basis for distinguishingevents from multiple sensors while being largely immune frominterference from other nodes in the network.

In a simulation testbed designed to quantify the performance of an ASBITnetwork, we first synthesized all the relevant I/Q waveforms for a largenumber of sensors. We allowed for realistic operating conditions byincluding relevant features of a particular microchip we fabricatedrecently as a candidate for implantable neural sensors. A description ofthe synthesis is given in FIG. 10 and FIG. 11 . The fabricated sub-mmsize microchips consists of rectifier circuits to inductively harvestwireless RF energy, a free-running oscillator for the clock in a digitalfinite state machine (FSM), and a toggle-type modulator to generate thebackscattered signal (overview in FIG. 12 ). For microscale sensors,especially those for biomedical implant purposes, housing an onboardhigh precision crystal oscillator or sophisticated clock stabilizationcircuitry is generally impractical due to size and power constraints. Incase of the ultralow power, small footprint free-running oscillator inour ASICs, we had to account for clock frequency variance and drift inthe simulations. The variance is caused by variations in chip power dueto the near-far problem in energy harvesting (i.e. distance dependenceof the transmitter (Tx) to a given sensor); the drift is caused byfluctuations in chip voltage supply due to circuit instabilities andfluctuations in ambient temperature. In particular since the unregulatedon-chip voltage supply (VDD) is linearly dependent on captured RFenergy, variations in VDD shift the clock frequency: a sensor closer tothe RF hub benefits from a higher VDD resulting in higher amplitude andhigher clock frequency of the backscattered signal.

The simulation testbed allowed us to systemically analyze key aspects ofthe proposed ASBIT protocol for a network on the scale of thousands ofmicrochip sensors. The details of the computational pipeline forensemble RF demodulation are illustrated in FIG. 13 in the case of afree-running oscillator as summarized below. To quantify the accuracy ofdata transmission, we define here an Event Error Rate (EER) as thenumber of errors per second while assuming a maximum event rate of 1kHz. A missing event or any instance of false detection was counted asan event error. A number of factors impacting the EER were examinedinsofar as the accuracy of event detection via the demodulation process.We assumed somewhat arbitrarily a nominal duration of each event (binsize) is 1 msec.

We quantified how varying the overall network size impacts the fidelityof communication as a function of noise, i.e. the EER vs. thesignal-to-noise ratio (SNR). As an example, “b” in FIG. 12 shows how areasonably low EER of 10-3 can be achieved with one thousand nodes inthe network. We note that while not near telecom values, an EER on theorder of 10-3 is considered acceptable for a forward model-basedapplication such as BMI-based neural prostheses. In the simulation, eachnode was assumed to be sparse, transmitting its signal at a 5% dutycycle, i.e. a statistical average event rate of 50 Hz with an eventtransmission duration of 1 msec (bin). “c” in FIG. 12 gives astatistical summary in terms of the quartile plots for a range of EERvalues across a population of 1,000 sensor nodes. The plot shows howmost nodes lie near the median while a few outliers show a much lowerEER. Next, we compared the situation between the case when the level ofharvested RF power is coupled to the clock (frequency) vs. the casewhere the two are uncoupled. When the two are coupled (dependentvariables), a lower EER is obtained for the ASBIT network compared tothe uncoupled case (independent variables). Details of the analysis ofthe EER across the nodes are shown in FIG. 14 as a function of the clockfrequency (in MHz) and the backscattered amplitude for the case when theharvested power and the clock are independent. As a multidimensionalsummary, FIG. 12 and FIG. 15 show how the aggregate sensor populationevent rate relates to the network capacity for the number of nodesranging from 250 to 2,000; the plot was generated by multiplying thestatistical event (‘firing’) rate by the number of nodes in evaluatingthe EER for the total network. As expected, the EER increases both withincreasing firing rate and the number of nodes. Overall, it is theaggregate event sum that mainly determines the network communicationperformance. This result suggests that the ASBIT protocol can beflexible with a simple scaling rule: A smaller number of sensors allowsfor high event activity rates while a greater number constrains thenetwork to sparser event rates.

At the same time, clock frequency drift and fluctuations add notinsignificant computational complexity to demodulation and decoding inthe ASBIT protocol. For the types of free-running on-chip oscillatorsused in our recent work, clock drifts can range anywhere from a few ppmto parts in a thousand in a given time interval. In that sense the abovesimulations summarized in FIG. 12 are idealized since clock drifts canaffect the accuracy of communication. We evaluated the impact of on-chipclock drift on the same simulation testbench. FIG. 12 summarizes thecomputed EER over a range of average clock drift (in units of ppm, thenominal clock frequency of 30 MHz), under the assumption that the clockfrequency can change randomly between individual transmission eventsfrom zero up to about ±1,005 ppm. The graph shows the penalty imposed byclock instability on the EER as a function of the bit length of the Goldcode. For example, when the clock drift is below 134 ppm, using a Goldcode of 512-bit length results in an EER lower than 10-3.5. Generally,as a design guide, a longer Gold code shows higher susceptibility toclock drift. Clock drift can be compensated at least up to a point byresorting to multiple MFs to account for the corresponding variances inthe backscattered waveforms as shown in FIG. 12 . Here, by using 31 setsof MFs (@2 kHz resolution), we could achieve 10-3.75 EER even with aclock drift of 1,005 ppm. However, the penalty incurred in using a largenumber of MFs is an increase in the computational burden in signalprocessing which can contribute to an increase in the overall systemlatency.

We designed a prototype wireless sensor ASIC to validate in in silicothe simulation predictions of the ASBIT communication method. The designof the ultra-low-power, sub-mm sized, system-on-chip silicon dieincorporated main pieces in FIG. 12 , namely a low-voltage rectifier, aGold code generator, a digital finite state machine, plus a BPSK-basedmodulator for backscattering. The ‘communication’ chips were fabricatedin TSMC's 65 nm mixed-signal/RF low-power CMOS process.

The diagram of FIG. 13 shows the implementation of our Gold codegenerator, here for 1023 bits, using preferred pairs of m-sequences andlinear feedback shift resistors (LFSR). To generate a uniquequasi-orthogonal sequence for each sensor without resorting to chippost-processing, we configured a physical unclonable function (PUF) toseed the Gold code generator. Each sensor chip uses its own 10-bit PUFto synthesize a 1023-bit unique Gold code. An advantage of using thisapproach is that the very small footprint compared to e.g. apseudo-random number generator (see FIG. 17 for the actual ASIC layout).FIG. 16 shows the footprint of the fabricated prototype 600 μm×600 μmCMOS chip. Most of the chip area is reserved for a capacitor bank tostabilize the voltage supply; the digital finite-state machine (FSM)only requires an area of some 50 μm×50 μm. The circuit parameters inthis particular ASIC were set to generate Gold code backscatteredtransmission every 20 μsec as shown in FIG. 18 so as to compareexperimental results with the simulations. FIG. 16 shows a piece ofmeasured I/Q data (on μsec timescale of a Gold code packet) from thewireless chip in comparison with output from the synthesizer tool wedeveloped, to demonstrate that the tool used in the RF simulation iscapable of regenerating the I/Q data from the chips at high fidelity.

We then characterized the bit error rate (BER) for the fabricated chips,each encoding a total 2047-bit Gold code, PUF-seeded sequence. We testedthe consistency of the chips in generating the exact same Gold codepattern over a finite length of time. For a meaningful statistical testwe measured a total of 18 post-processed wireless chips, the datasummarized in the histogram of FIG. 16 . Most of the chips achieved BERbelow 10-4 in a demonstration of Gold code performance at a sufficientlevel of accuracy e.g. for BMI use. The plot of FIG. 16 also indicatesthe spread of clock frequencies across this ensemble of 18 chips,frequency ranging from 31 MHz to 33 MHz and being dependent on theincoming RF level. We also characterized in further statistical detailthe clock drift over time for three randomly chosen chips and found thisto be around ±1,000 ppm (FIG. 16 ). Due to the finite drift, thewaveform of the Gold code packet for any given chip varied acrossindividual transmission events whereby the correlation values obtainedusing one matched filter became inaccurate. However, the use of multiplesets of MFs to compensate for clock drift led to a comparablecorrelation output for all packet transmission events as demonstrated inFIG. 18 . The statistical plot of FIG. 16 shows the peak MF outputduring the packet transmission compared to its RMS level during anon-transmission period, normalized for each chip, and the dependence onthe number of matched filters. Note how using only a single MF yield avalue range from 0.1 to 1 so that some recovered events yielded only 10%of the maximum possible correlation value. By contrast, when thedemodulation deployed a set of 31 MFs, outliers were captured as well(gray colored plot in FIG. 16 ) whereby 75% of events achieved 85% valuerelative to the maximum correlation value.

From the population of post-processed 18 chips, we chose four chips forfurther experiments (each reliably transmitting a non-overlapping Goldcode signal every 20 milliseconds). FIG. 18 (for 4 MFs) shows theamplitude of the received transient RF signal and the correlation outputfrom MF sets specifically designed for each chip. One sees how thismethod differentiates a target packet even when two packets haveundergone an interfering collision. We added a proxy noise level tomimic the interference from a background ensemble of chips. FIG. 16shows the recovery of clock frequency from four chips even in thepresence of an SNR=−28.77 dB, an equivalent noise contribution by 480chips in the wireless network, each with an SNR of 3.22 dB (FIG. 19 ).We also tested the ability to detect a packet sequence within a shorttime interval, here corresponding to transmission of 1 to 5 Gold codepackets (FIG. 16 ). The experiment showed how one can achieve a 100%detection rate from four chips even with an SNR of −26.77 dB, equivalentto 300 chips active in the overall network. Last, we evaluated thevariance in EER for the set of four chips as illustrated in FIG. 16using 2047-bit or 511-bit Gold codes, respectively. We could achieveEER=10-3.55 for an SNR of −24.77 dB, equivalent to 180 or 720 otherbackground nodes running in the network, respectively. In sum, theexperiments support the results which show that, even with the penaltiesimposed by clock drift and fluctuations, the ASBIT protocol is scalableto hundreds of nodes and capable of operating in a relatively modest SNRenvironment, yet quite immune from collisions.

As an alternative to the on-chip reliance of free-running clocks, weinvestigated role of the RF baseband downlink as possible frequencyreference for circumstance where this is potentially advantageous andtechnically practical. Using the baseband RF for timing has beendemonstrated for passive RFID tags whereby an incoming RF frequency(here 900 MHz) is down-converted to generate a lower frequency clock.One particular choice is a multiple stage True Single Phase Clocked(TSPC) frequency divider shown schematically in FIG. 21 as embedded inour monolithic sensor. A divider approach offers the benefit ofnegligible clock variance and nearly independence from energy harvestingefficiency. Note that a frequency divider approach does not implynetwork synchrony since the phases of individual on-chip frequencyreferences (clocks) will differ due to phase lag arising e.g. fromrandom start-up in each chip's starting circuit. Here we show results ofa simulation-based analysis in assessing the performance of the ASBITnetworking protocol while assuming identical frequency divider circuitsfor all sensors.

Given an expected clock frequency for each sensor, we can estimate thetiming sequences across the sensor ensemble as the backscattered signalare generated. In contrast to the case of sensors with on-chiposcillators, the ASBIT demodulation step can now be performedcomputationally rather simply. FIG. 21 shows how applying a single MF ina predefined time window allows the direct exclusion of false detectionhence improving the EER. FIG. 21 in particular shows how, in the clockdivider approach, our ASBIT protocol can readily accommodate asignificantly larger number of nodes compared to the case of an on-chiposcillator. In this example an ensemble of 4,000 sensors can achieve anEER of 10-4.5 for an SNR is 3.33 dB assuming a 50 Hz average eventdetection rate for each node. Further, the total aggregate event (spike)rate across the network, i.e. the network capacity, can now be increasedas illustrated in the composite FIG. 21 . If we again were to assumethat an EER on the order of 10-3 is acceptable for an application suchas BMI-based neural prostheses, the simulations predict that the ASBITprotocol can communicate an aggregate of up to 4×105 spike events persecond. The expected value of EER does depend on the length of the Goldcode; FIG. 23 shows predictions which suggests an optimal range ofaround 1024 bits to 2048 bits depending on the size of the network.

We also re-examined the near-far problem, here for a network ofinductively powered RF sensors. We first fixed the RF amplitude ratiobetween ‘near’ sensors and ‘far’ sensors as 2:1. The results are shownin FIG. 21 where e.g. an EER of 10-3.8 can be achieved in the networkconsisting of 4,000 nodes for a 6:1 near/far ratio in the backscatteredsignal amplitudes. FIG. 23 shows that the most of bit errors occur atthe “far” sensors while “near” sensors are unaffected by the othernodes. A practical aspect in the near-far problem arises fromlimitations in hardware performance such as bit resolution and dynamicrange and other details at the transceiver hub. We note that in mostsoftware-defined radios (SDR), an automatic gain control scales theincoming data for the ADC and thus determines the dynamic range based onthe strongest signal from the ‘near’ sensors. In FIG. 21 , we analyzedthe performance of the ASBIT for a range of various ADC resolutions toshow how an 8-bit ADC can achieve an EER comparable to in a 16-bit ADC.The outcome may be due to effective bit protection by the Gold codesequence which spreads out the signal in time domain while also assumingthat linear superposition holds for signals from multiple nodes.

It would be appreciated by those skilled in the art that various changesand modifications can be made to the illustrated embodiments withoutdeparting from the spirit of the present invention. All suchmodifications and changes are intended to be within the scope of thepresent invention except as limited by the scope of the appended claims.

What is claimed is:
 1. A method comprising: providing an ensemble of distributed sensors; delivering radio frequency (RF) power to each sensor by inductive near-field coupling by a magnetic field projected by an epidermal transmit (Tx) coil; in each individual sensor, detecting a sparse binary event in its immediate environment; reporting the detected sparse binary event to an external RF receiver hub asynchronously and with low latency; and minimizing error rates due to statistical data packet collisions in asynchronous telemetry by digitally encoding each sensor according to a particular address scheme where each address is one function from an infinite set of mathematically orthogonal functions, enabling a simultaneous detection from up to ten thousand points without interference at a common receiver.
 2. The method of claim 1 wherein each of the sensors has an application-specific integrated circuit (ASIC) implemented as a system-of-chip in monolithic silicon that combine sensor circuits with RF telemetry circuits on one integrated microscale platform.
 3. The method of claim 2 wherein the ASIC is designed for binary phase shift key (BPSK) or other types of digital signal modulation which accommodates encoding of the set of orthogonal address functions.
 4. The method of claim 1 wherein the ensemble of distributed sensors is implanted in a body or on a body.
 5. The method of claim 1 wherein the ensemble of distributed sensors is applied to a surface of a skin or below the skin as a distributed implant.
 6. A system comprising: a plurality of independent sensors, each of the independent sensors digitally encoded on-chip according to a particular address scheme; an epidermal transmit (Tx) coil, the Tx coil delivering radio frequency (RF) power to each of the plurality of independent sensors, the Tx coil capturing asynchronous data emitted from each of the plurality of sensors by radio frequency (RF) backscattering; and an external radio frequency (RF) receiver hub.
 7. The system of claim 6 wherein each of the independent sensors is an application-specific integrated circuit (ASIC).
 8. The system of claim 7 wherein the ASIC is designed for binary phase shift key (BPSK) modulation.
 9. The system of claim 6 wherein the plurality of independent sensors is implanted in a body or on the body.
 10. The system of claim 6 wherein the plurality of independent sensors is applied to a surface of a skin.
 11. A method comprising: providing a communication protocol between an external RF transceiver hub and an ensemble of distributed individual sensors; in each individual sensor, detecting a sparse binary event in its immediate environment; reporting the detected sparse binary event to an external RF hub asynchronously and with low latency; and minimizing error rates due to statistical data packet collisions in asynchronous telemetry by digitally encoding each sensor according to a particular address scheme where each address is one function from an infinite set of mathematically random or orthogonal functions, enabling the simultaneous detection from up to ten thousand points without interference at the common receiver. 